{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as mpt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datas = pd.read_csv('data/horse.csv', header=None, names=['x1', 'x2', 'y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         x1         x2  y\n",
      "0 -0.017612  14.053064  0\n",
      "1 -1.395634   4.662541  1\n",
      "2 -0.752157   6.538620  0\n",
      "3 -1.322371   7.152853  0\n",
      "4  0.423363  11.054677  0\n"
     ]
    }
   ],
   "source": [
    "print datas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive=datas[datas['y'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "negtive = datas[datas['y'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xbb4bc18>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = mpt.subplots(figsize=(10,5))\n",
    "ax.scatter(positive['x1'], positive['x2'], s = 30, c = 'b', marker = 'o', label='Admitterd' )\n",
    "ax.scatter(negtive['x1'], negtive['x2'], s = 30, c = 'r', marker = 'x', label='Not Admitterd' )\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
